AI - Apple Silicon Mac M1/M2 机器学习环境 (TensorFlow, JupyterLab, VSCode)

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注：由于 https://github.com/apple/tensorflow_macos 已经 archived，建议大家根据 Apple Silicon Mac M1/M2 原生支持 TensorFlow 2.10 GPU 加速（tensorflow-metal PluggableDevice）安装最新支持 GPU 加速的 TensorFlow。

Xcode
Command Line Tools
Homebrew
Miniforge
下载 Apple TensorFlow
创建虚拟环境
安装必须的包
安装特殊版本的 pip 和其他包
安装 Apple 提供的包(numpy, grpcio, h5py)
安装额外的包
安装 TensorFlow
测试
JupyterLab
VSCode
延伸阅读
参考

Xcode

从 App Store 安装 Xcode。

Command Line Tools

从 Apple Developer 下载安装 Xcode Command Line Tools 或者执行以下命令。

$ xcode-select --install

Homebrew

$ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Miniforge

Anaconda 无法在 M1 上运行, Miniforge 是用来替代它的。

从 https://github.com/conda-forge/miniforge 下载 Miniforge3-MacOSX-arm64。

执行以下命令，安装 Miniforge

$ bash Miniforge3-MacOSX-arm64.sh

重启终端并检查 Python 安装情况。

$ which python
/Users/catchzeng/miniforge3/bin/python
$ which pip
/Users/catchzeng/miniforge3/bin/pip

下载 Apple TensorFlow

从 https://github.com/apple/tensorflow_macos/releases 下载 TensorFlow 并解压，然后进入 arm64 目录下。

创建虚拟环境

创建一个 conda 创建虚拟环境，这里使用 python 3.8 (ATF 2.4 需要)。

$ conda create -n tensorflow python=3.8
$ conda activate tensorflow

安装必须的包

$ brew install libjpeg
$ conda install -y pandas matplotlib scikit-learn jupyterlab

注意: libjpeg 是 matplotlib 需要依赖的库。

安装特殊版本的 pip 和其他包

$ pip install --force pip==20.2.4 wheel setuptools cached-property six packaging

注意: Apple TensorFlow 特殊版本的 pip。

安装 Apple 提供的包(numpy, grpcio, h5py)

$ pip install --upgrade --no-dependencies --force numpy-1.18.5-cp38-cp38-macosx_11_0_arm64.whl grpcio-1.33.2-cp38-cp38-macosx_11_0_arm64.whl h5py-2.10.0-cp38-cp38-macosx_11_0_arm64.whl

安装额外的包

$ pip install absl-py astunparse flatbuffers gast google_pasta keras_preprocessing opt_einsum protobuf tensorflow_estimator termcolor typing_extensions wrapt wheel tensorboard typeguard

安装 TensorFlow

$ pip install --upgrade --no-dependencies --force tensorflow_macos-0.1a3-cp38-cp38-macosx_11_0_arm64.whl
$ pip install --upgrade --no-dependencies --force
tensorflow_addons_macos-0.1a3-cp38-cp38-macosx_11_0_arm64.whl

最后，升级 pip 到正确的版本。

$ pip install --upgrade pip

测试

TensorFlow

$ python
Python 3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 15:50:57)
[Clang 11.0.1 ] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> print(tf.__version__)
2.4.0-rc0
>>>

JupyterLab

$ jupyter lab

from tensorflow.keras import layers
from tensorflow.keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()

from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)
test_loss, test_acc = model.evaluate(test_images, test_labels)
test_acc

VSCode

安装 Python 支持

选择虚拟环境并信任 notebook

运行 notebook